Workflow Orchestration for Regulatory Affairs

Data Characteristics in This Category

Regulatory affairs pharmacovigilance data originates from clinical trial reports, post-market surveillance data, safety update reports (PSUR/PBRER), individual case safety reports (ICSR), literature search results, and regulatory guidelines. This data typically exists in a mixed format, including structured data (e.g., database records, XML files) and unstructured data (e.g., PDF documents, Word reports, scanned images). Update frequency varies; clinical trial data is summarized periodically as trials progress, post-market data is collected continuously, and safety update reports are usually submitted semi-annually, annually, or over longer periods. Document structures are complex. For example, ICSR reports contain fields such as patient information, drug information, adverse event descriptions, and medical assessments. PSUR/PBRERs include sections like aggregated data, signal detection results, and risk-benefit evaluations. Field content is diverse, involving medical terminology, drug generic/brand names, dosage units (e.g., mg, IU), time units (e.g., days, weeks, months), and adverse reaction coding (e.g., MedDRA).

Constraints Imposed by These Characteristics on Workflow Orchestration

The diversity and complexity of regulatory affairs data impose specific requirements on workflow orchestration. Structured data requires precise field mapping and data cleansing to ensure accurate information flow between different systems. Parsing unstructured documents relies on advanced Natural Language Processing (NLP) capabilities, such as extracting key information from PDF reports, which requires the workflow to integrate document parsing nodes. Data update frequency determines the workflow's trigger mechanism; for example, scheduled tasks can trigger workflows for periodic safety reports, while ICSRs may require real-time or near real-time triggers. The application of specialized coding systems like MedDRA means workflows need capabilities for terminology standardization and coding validation when processing adverse event descriptions. Additionally, regulatory compliance requires all data processing steps to be traceable, necessitating detailed workflow logging. Document structural complexity requires workflows to handle multi-level document parsing and information extraction, and support conditional branching to manage different report types.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`maxContext`	`3000-5000 characters`	Ensures the LLM can process longer paragraphs in regulatory documents, preventing loss of critical information.
`Chunk size` (Chunk Size)	`800-1200 characters`	Balances chunk granularity, ensuring each chunk contains sufficient context while avoiding excessive length that reduces model processing efficiency.
`Recall count` (Recall Count)	`top 5`	Focuses on the most relevant document snippets, reducing unnecessary background information and improving retrieval efficiency.
`Similarity threshold` (Similarity Threshold)	`0.75-0.85`	Filters out highly relevant document content and excludes noise, especially for precise matching of medical terminology.
`Rerank result count` (Rerank Count)	`top 3`	Further optimizes ranking based on recall, ensuring the most core information is presented first.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Accounts for the possibility of large regulatory documents (e.g., PSURs) with many pages, allocating sufficient parsing time.

Three Common Pitfalls

LLM node returns chat:LLM_model_response_empty: This usually occurs when the input prompt or context is too long, exceeding the model's processing capacity, or due to an internal model error.
Boolean judgment node result does not match expectations: The phenomenon is that node one outputs true, but the connected judgment node shows false. This may be because the output type of node one is inconsistent with the type expected by the judgment node, such as the difference between the string "true" and the boolean value true.
Workflow encounters Error: Timeout: This may be due to file parsing, API calls, or database queries taking too long, exceeding the maximum waiting time set for the workflow or node.

How to Verify Configuration

For key information extraction tasks, test with different types of regulatory documents (e.g., ICSR, PSUR) and verify that the extraction results match the original content.
Simulate abnormal data input (e.g., missing fields, format errors) to verify that the workflow's error handling mechanism triggers as expected and logs error information.
Check workflow logs to confirm the execution status, input/output, and duration of each node, ensuring no unexpected timeouts or failures.
Use a test set containing specific medical terminology and codes (e.g., MedDRA codes) to verify the workflow's accuracy in terminology standardization and code matching, adjusting thresholds based on business needs.

Note: The values provided are common starting points and should be measured against specific samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.